Results 1 - 10 of 77

Detection of Outlier-Communities using Minimum Spanning Tree

by S. Chidambaranathan, S. John Peter
"... Community (also known as clusters) is a group of nodes with dense connection. Detecting outlier-communities from database is a big desire. In this paper we propose a novel Minimum Spanning Tree based algorithm for detecting outlier-communities from complex networks. The algorithm uses a new communit ..."
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Community (also known as clusters) is a group of nodes with dense connection. Detecting outlier-communities from database is a big desire. In this paper we propose a novel Minimum Spanning Tree based algorithm for detecting outlier-communities from complex networks. The algorithm uses a new

Outlier Removal Clustering through Minimum Spanning Tree

by T. Karthikeyan, S. John Peter
"... Minimum spanning tree-based clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outliers using clustering algorithm is a big desire. Outlier detection is an extremely important task in a wide variety of application. In this paper we propose a minimum spanning t ..."
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Minimum spanning tree-based clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outliers using clustering algorithm is a big desire. Outlier detection is an extremely important task in a wide variety of application. In this paper we propose a minimum spanning

Discovering Local Outliers using Dynamic Minimum Spanning Tree with Self-Detection of Best Number of Clusters

by S. John Peter
"... Detecting outliers in database (as unusual objects) using Clustering and Distance-based approach is a big desire. Minimum spanning tree based clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose a new algorithm to detect outliers based on minimum ..."
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Detecting outliers in database (as unusual objects) using Clustering and Distance-based approach is a big desire. Minimum spanning tree based clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose a new algorithm to detect outliers based on minimum

Clustering the Labeled and Unlabeled Datasets using New MST based Divide and Conquer Technique

by Srinivasulu Asadi, V. Saikrishna, Bhudevi Aasadi
"... Abstract: Clustering is the process of partitioning the data set into subsets called clusters, so that the data in each subset share some properties in common. Clustering is an important tool to explore the hidden structures of modern large Databases. Because of the huge variety of the problems and ..."
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of computation and the standard solutions take O(N 2) time. In our paper, we present a fast minimum spanning tree-inspired clustering algorithm. This algorithm uses an efficient implementation of the cut and the cycle property of the NMST, that can have much better performance than O(N 2) time.

Meta Similarity Fine Clusters Using Dynamic Minimum Spanning Tree with Self-Detection of Best Number of Clusters

by S. John Peter
"... Clustering is a process of discovering groups of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input ..."
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to their input parameters. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outlier in database (as unusual objects) is a big desire. In data mining detection of anomalous pattern in data is more interesting than detecting inliers. In this paper I

Meta Similarity Noise-free Clusters Using Dynamic Minimum Spanning Tree with Self-Detection of Best Number of Clusters

by T. Karthikeyan, S. John Peter
"... Clustering is a process of discovering group of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input ..."
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parameters. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outlier in database (as unusual objects) is a big desire. In data mining detection of anomalous pattern in data is more interesting than detecting inliers. In this paper we propose a

An evolutionary algorithm with solution archive for the generalized minimum spanning tree problem

by Bin Hu, Günther R. Raidl - Proceedings of the 13th International Conference on Computer Aided Systems Theory: Part I, volume 6927 of LNCS , 2012
"... We consider the recently proposed concept of enhancing an evolutionary algorithm (EA) with a complete solution archive. It stores evaluated solutions during the optimiza-tion in order to detect duplicates and to efficiently transform them into yet unconsidered solutions. For this approach we introdu ..."
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are obtained via primal and dual heuris-tics. As an application we consider the generalized min-imum spanning tree problem where we are given a graph with nodes partitioned into clusters and exactly one node from each cluster must be connected in the cheapest way. As the EA uses operators based on two dual

OPTIMAL DUAL SIMILARITY NOISE-FREE CLUSTERS USING DYNAMIC MINIMUM SPANNING TREE

by S. John Peter
"... Clustering is a process of discovering groups of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input ..."
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to their input parameters. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outlier in database (as unusual objects) is a big desire. In data mining detection of anomalous pattern in data is more interesting than detecting inliers. In this paper we

New Techniques for Geographic Routing

by Ben Wing, Ben Wing, Lup Leong, Lup Leong , 2006
"... As wireless sensor networks continue to grow in size, we are faced with the prospect of emerging wireless networks with hundreds or thousands of nodes. Geographic routing algorithms are a promising alternative to tradition ad hoc routing algorithms in this new domain for point-to-point routing, but ..."
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As wireless sensor networks continue to grow in size, we are faced with the prospect of emerging wireless networks with hundreds or thousands of nodes. Geographic routing algorithms are a promising alternative to tradition ad hoc routing algorithms in this new domain for point-to-point routing

A Grouping Principle and Four Applications

by Agnè S Desolneux , Lionel Moisan , Jean-Michel Morel - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2003
"... Abstract-Wertheimer's theory suggests a general perception law according to which objects having a quality in common get perceptually grouped. The Helmholtz principle is a quantitative version of this general grouping law. It states that a grouping is perceptually "meaningful" if its ..."
Abstract - Cited by 59 (6 self) - Add to MetaCart
of the Meaningfulness of Each Cluster In the minimum spanning tree, each subtree associated to a root node A with value corresponds to a -cluster (named A 0 ) made of the connected union of the disks with radius =2 centered on the points encountered in the subtree. We compute the meaningfulness (À log 10 NF A) of each
Results 1 - 10 of 77